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    Coursera
    1. Sequence Models
    2. Week 1
    3. Recurrent Neural Networks
    PreviousNext
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        Quiz: Recurrent Neural Networks
        10 questions
    QuizQuiz • 30 min30 minutes

    Recurrent Neural Networks

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    Recurrent Neural Networks
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    Recurrent Neural Networks

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    1.
    Question 1

    Suppose your training examples are sentences (sequences of words). Which of the following refers to the jthj^{th}jth word in the ithi^{th}ith training example?

    1 / 1 point
    Check
    Correct

    We index into the ithi^{th}ith row first to get the ithi^{th}ith training example (represented by parentheses), then the jthj^{th}jth column to get the jthj^{th}jth word (represented by the brackets).

    2.
    Question 2

    Consider this RNN:

    This specific type of architecture is appropriate when:

    1 / 1 point
    Check
    Correct

    It is appropriate when every input should be matched to an output.

    3.
    Question 3

    To which of these tasks would you apply a many-to-one RNN architecture? (Check all that apply).

    1 / 1 point
    Check
    Correct

    Correct!

    Check
    Correct

    Correct!

    4.
    Question 4

    You are training this RNN language model.

    At the ttht^{th}tth time step, what is the RNN doing? Choose the best answer.

    1 / 1 point
    Check
    Correct

    Yes, in a language model we try to predict the next step based on the knowledge of all prior steps.

    5.
    Question 5

    You have finished training a language model RNN and are using it to sample random sentences, as follows:

    What are you doing at each time step ttt?

    1 / 1 point
    Check
    Correct
    6.
    Question 6

    You are training an RNN, and find that your weights and activations are all taking on the value of NaN (“Not a Number”). Which of these is the most likely cause of this problem?

    1 / 1 point
    Check
    Correct
    7.
    Question 7

    Suppose you are training a LSTM. You have a 10000 word vocabulary, and are using an LSTM with 100-dimensional activations a<t>a^{<t>}a<t>. What is the dimension of Γu\Gamma_uΓu​ at each time step?

    1 / 1 point
    Check
    Correct

    Correct, Γu\Gamma_uΓu​ is a vector of dimension equal to the number of hidden units in the LSTM.

    8.
    Question 8

    Here’re the update equations for the GRU.

    Alice proposes to simplify the GRU by always removing the Γu\Gamma_uΓu​. I.e., setting Γu\Gamma_uΓu​ = 1. Betty proposes to simplify the GRU by removing the Γr\Gamma_rΓr​. I. e., setting Γr\Gamma_rΓr​ = 1 always. Which of these models is more likely to work without vanishing gradient problems even when trained on very long input sequences?

    1 / 1 point
    Check
    Correct

    Yes. For the signal to backpropagate without vanishing, we need c<t>c^{<t>}c<t> to be highly dependent on c<t−1>c^{<t-1>}c<t−1>.

    9.
    Question 9

    Here are the equations for the GRU and the LSTM:

    From these, we can see that the Update Gate and Forget Gate in the LSTM play a role similar to _______ and ______ in the GRU. What should go in the blanks?

    1 / 1 point
    Check
    Correct

    Yes, correct!

    10.
    Question 10

    You have a pet dog whose mood is heavily dependent on the current and past few days’ weather. You’ve collected data for the past 365 days on the weather, which you represent as a sequence as x<1>,…,x<365>x^{<1>}, …, x^{<365>}x<1>,…,x<365>. You’ve also collected data on your dog’s mood, which you represent as y<1>,…,y<365>y^{<1>}, …, y^{<365>}y<1>,…,y<365>. You’d like to build a model to map from x→yx \rightarrow yx→y. Should you use a Unidirectional RNN or Bidirectional RNN for this problem?

    1 / 1 point
    Check
    Correct

    Yes!